Using Self-Service Business Intelligence Tools and Data Visualizations to Facilitate Multicenter Sleep Research Ric Miller COMET - Director of Informatics Stanford Sleep Medicine Center

Conflict of Interest Disclosures for Speakers X 1. I do not have any relationships with any entities producing, marketing, re- selling, or distributing health care goods or services consumed by, or used on, patients, OR 2. I have the following relationships with entities producing, marketing, re- selling, or distributing health care goods or services consumed by, or used on, patients. Type of Potential ConflictDetails of Potential Conflict Grant/Research Support Consultant Speakers’ Bureaus Financial support Other 3. The material presented in this lecture has no relationship with any of these potential conflicts, OR 4. This talk presents material that is related to one or more of these potential conflicts, and the following objective references are provided as support for this lecture:

COMET Comparative Outcomes Management with Electronic data Technology supported by AHRQ Grant number 1R01HS019738

COMET 1.Multicenter research (Stanford, University of Pennsylvania, University of Wisconsin ‐ Madison, Harvard) randomized trial comparing obstructive sleep apnea treatments: positive airway pressure (PAP) vs. oral appliance (OA) therapy in 196 randomized participants 2.Informatics platform that facilitates electronic data management for multicenter clinical trials, enabling the generation of meaningful, well ‐ defined outcomes structured for use with business intelligence (BI) tools

Self-Service Business Intelligence Business intelligence (BI) is a set of tools and techniques used to transform datasets into information that is frequently visual in nature. “Self-service” refers to the ability of researchers to create their own data visualizations after a dataset has been provided which facilitates low cost, high quality clinical analytics. Interactive data visualizations, produced with BI tools are useful in bringing the data closer to the researcher and providing unique insights into the data but require standardized methods to be effective.

Self-Service Business Intelligence 1.Data Pipeline 2.Terminology 3.Data Visualization

Data Pipeline

ETL (Extract Transform Load) Flat FileStar Schema Data Pipeline Relational Database ETL (Extract Transform Load)

Terminology

How many SDID by VisitName? -vs- How many patients by visit?

Terminology

Data Visualization Dashboards KPI (Key Performance Indicators) Printable Reports Interactive Data Visualizations

Data Visualization

Dimensions - FiltersFacts - Measures

Data Visualization

COMET Hub

Acknowledgements Authors: Miller RA, Nichols, DA, Griffin KS, Benca RM, Kuna ST, Mignot E, Pack AI, Redline S, Yang P, Hyde, PR, Walsh JK, Kushida, CA COMET: Supported by AHRQ Grant number 1R01HS019738, Clete Kushida, M.D., Ph.D.,PI SMART DOCS: Supported by PCORI, Clete Kushida, M.D., Ph.D.,PI COMET Hub: Pending evaluation by AHRQ for support, Miller RA, PI

Using Self-Service Business Intelligence Tools and Data Visualizations to Facilitate Multicenter Sleep Research Ric Miller COMET - Director of Informatics Stanford Sleep Medicine Center